The second thing I'm excited to talk about is qualitative learning. One of the hard hard problems in AI engineering is building evals. And there's a few reasons why this is hard. One is that defining what good looks like is really challenging for any real-world problem. The second is that when you define good, often times you have to lose detail. If an email is good or bad, contains a lot less information than if you know what could change in that email in order to improve. And the third is that whenever you create a hill in a dataset, you're really trying to create a proxy for reality. What if instead we could use reality to inform our evals automatically? What qualitative learning asks is how do we decrease this question? How do we decrease assistance? And it's a research question right now. But what we believe is that models are now good enough to interpret whatever textual feedback is present in the environment and convert that into evals and a hill that the model can climb. And so, as you get more feedback from production, it's traces, it's user actions, it's product analytics, it's asking you it's the model asking you questions about how data should be represented. As you do this, the model can iteratively refine the hill over time and continue climbing it to solve your actual business problem.